Bayesian Artificial Intelligence, Second Edition by Kevin B. Korb

By Kevin B. Korb

Updated and accelerated, Bayesian man made Intelligence, moment Edition offers a pragmatic and available creation to the most techniques, starting place, and functions of Bayesian networks. It specializes in either the causal discovery of networks and Bayesian inference methods. Adopting a causal interpretation of Bayesian networks, the authors speak about using Bayesian networks for causal modeling. additionally they draw all alone utilized examine to demonstrate a number of functions of the technology.

New to the second one Edition

    • New bankruptcy on Bayesian community classifiers
    • New part on object-oriented Bayesian networks
    • New part that addresses foundational issues of causal discovery and Markov blanket discovery
    • New part that covers equipment of comparing causal discovery programs
    • Discussions of many universal modeling errors
    • New functions and case studies
    • More assurance at the makes use of of causal interventions to appreciate and cause with causal Bayesian networks

    Illustrated with genuine case experiences, the second one version of this bestseller maintains to hide the basis of Bayesian networks. It provides the weather of Bayesian community expertise, automatic causal discovery, and studying percentages from facts and exhibits tips to hire those applied sciences to enhance probabilistic professional systems.

    Web Resource
    The book’s web site at www.csse.monash.edu.au/bai/book/book.html deals various supplemental fabrics, together with instance Bayesian networks and knowledge units. teachers can electronic mail the authors for pattern ideas to a number of the difficulties within the text.

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    Additional resources for Bayesian Artificial Intelligence, Second Edition

    Example text

    9 — is only one term on the right hand side; the other crucial term is the prior probability of cancer. Cognitive psychologists studying such reasoning have dubbed the dominance of likelihoods in such scenarios “base-rate neglect,” since the base rate (prior probability) is being suppressed (Kahneman and Tversky, 1973). 043 Now the discrepancy between 4% and 80 or 90% is no small matter, particularly if the consequence of an error involves either unnecessary surgery or (in the reverse case) leaving a cancer untreated.

    You can’t simply examine both sides of the coin). , what’s the maximum number of tosses until that happens? ) Two cab companies, the Blue and the Green, operate in a given city. Eighty-five percent of the cabs in the city are Blue; the remaining 15% are Green. A cab was involved in a hit-and-run accident at night. A witness identified the cab as a Green cab. The court tested the witness’ ability to distinguish between Blue and Green cabs under night-time visibility conditions. It found that the witness was able to identify each color correctly about 80% of the time, but confused it with the other color about 20% of the time.

    Applications Problem 13 (Note: this is the case of Sally Clark, convicted in the UK in 1999, found innocent on appeal in 2003, and tragically died in 2007 of alcohol poisoning. ) A mother was arrested after her second baby died a few months old, apparently of sudden infant death syndrome (SIDS), exactly as her first child had died a year earlier. According to prosecution testimony, about 2 in 17200 babies die of SIDS. So, according to their argument, there is only a probability of (2/17200)2 ≈ 1/72000000 that two such deaths would happen in the same family by chance alone.

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